Detecting and Responding to Rendering of Interactive Video Content

ABSTRACT

A computing system obtains a fingerprint of video content being rendered by a video presentation device, including a first portion representing a pre-established video segment and a second portion representing a dynamically-defined video segment. While obtaining the query fingerprint, the computing system (a) detects a match between the first portion of the query fingerprint and a reference fingerprint that represents the pre-established video segment, (b) based on the detecting of the match, identifies the video content being rendered, (c) after identifying the video content being rendered, applies a trained neural network to at least the second portion of the query fingerprint, and (d) detects, based on the applying of the neural network, that rendering of the identified video content continues. And responsive to at least the detecting that rendering of the identified video content continues, the computing system then takes associated action.

REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. patent application Ser. No. 15/620,440,filed Jun. 12, 2017, the entirety of which is hereby incorporated byreference.

BACKGROUND

A typical video presentation device operates to receive a digital videostream representing video content and to render the video content on adisplay for viewing by one or more users. Examples of such devicesinclude, without limitation, televisions, computer monitors,head-mounted displays, tablets, smart phones, watches, cameras,projection systems, and the like.

In many cases, the video presentation device may be in communicationwith a video source that could selectively provide any of a variety ofvideo content for rendering, and the video presentation device could bearranged to receive and render the selected video content. For example,the video presentation device could be coupled or communicatively linkedwith a receiver, player, console, computer, and/or remote server that isconfigured to output video content selected by a user, and the videopresentation device could be configured to receive the video contentbeing output by the video source and to render the video content on adisplay in real-time for viewing.

Some video content could be at least partially “interactive,” where auser to whom the video content is presented interacts with the contentas it is being rendered, and the content varies dynamically based onthat user-interaction. Without limitation, an example of interactivevideo content is video game, where a user (a person who plays the game)has at least some control over how the game proceeds and thus what thevideo content of the game would be over time. For instance, throughinteraction with a game console, remote server, or other source of thegame's video content, a user might control video content associated withachieving or failing to achieve a goal of the game, gaining or losing anitem in the game, virtual orientation within the game, and/or movementor other actions of one or more avatars or objects in the game, amongnumerous other possibilities. As a result, the interactive video contentcould vary in real-time based on the user's input and could differ,possibly substantially, each time it is played.

Such user interaction and impact on the video content being rendered canbe distinguished from user control over the basic mechanics of playingthe video content, where the user's interaction has no impact on theunderlying video content itself. For example, a video source such as agame console or remote server might allow a user to control functionssuch as play, pause, stop, fast-forward, or fast-reverse. But thosecontrol functions would not impact the underlying video content andwould thus not be considered interactive, in contrast to real-time userinteraction controlling the story and progression of a video game forinstance.

Further, video content that includes such interactive ordynamically-defined content may also include from time to time certainpre-established or statically-defined video segments that do not varybased on user interaction while being rendered. For example, video gamesoften include pre-established “cutscenes,” such as cinematicallyproduced video clips, that are automatically played as transitionsbetween game levels, and other pre-established video segments such asstartup scenes, shutdown scenes, and the like. Although a user may havesome control over when these pre-established video segments are played,such as when the user successfully completes a level of play in a videogame, content of the pre-established video segments would not vary basedon user interaction while the pre-established video segments are beingrendered.

SUMMARY

When a video presentation device receives and renders video content, thevideo presentation device may not have an indication of the identity ofthe video content being rendered. A video source such as a local playeror remote server that provides the video content to the videopresentation device may have such information. But the videopresentation device that receives the video content from that videosource may have no such information.

For instance, if a computer monitor is connected with a video gameconsole and a user interacts with the game console to select aparticular video game to play, the game console may have an indicationof which game is being played and therefore which game's video contentis being output for rendering. But the computer monitor may merelyreceive and render the video content provided by the game console andmay have no indication that the video content being rendered is a videogame, let alone which video game is being played and therefore whichvideo game's content the computer monitor is rendering.

For various reasons, however, it may be useful to determine the identityof video content being rendered by a video presentation device. Further,it may be useful to do so without receiving from a video source a reportof which video content is being presented, and perhaps without anyinvolvement of the video source or its provider. For instance, it may beuseful for the video presentation device itself, and/or a network serverworking in cooperation with the video presentation device, to identifythe video content that the video presentation device is rendering, basedon an evaluation of the video content itself as it is being rendered.

Given knowledge of the identity of the video content that is beingrendered, the video presentation device or other entity couldprogrammatically carry out one or more useful actions, such as actionsspecific to the identified video content. For instance, the entity couldrecord the fact that the video presentation device is presenting theidentified video content, as part of a content ratings or analyticssystem to measure the extent to which particular video content ispresented. Alternatively, the entity could respond to particular videocontent being presented by triggering presentation of supplemental videocontent, such as a pop-up advertisements or other information related tothe identified content or otherwise based on the video content being theidentified video content.

By way of example, upon determining that the video content beingrendered is a particular video game, an entity could present a pop-upadvertisement offering virtual or physical merchandise or servicesrelated to that video game. Further, the entity could determine how longthe rendering of the game continues between cutscenes and, based on thatduration, could trigger presentation of supplemental content. Forinstance, if the duration is threshold long, the entity could triggerpresentation of game play help, such as hints or other tips forachieving goals in the game, on grounds that the user appears to bestruggling. Other examples are possible as well.

To facilitate this in practice, as the video presentation device isrendering video content, the video presentation device could generateand provide to a computing system a digital fingerprint of the videocontent being rendered. And as the computing system obtains thatfingerprint, the computing system could compare that fingerprint withreference fingerprint data established in advance for known videocontent items. In theory, if the computing system thereby determinesthat the fingerprint of the video content being rendered matches thereference fingerprint of a known video content item, the server couldthereby conclude that the video content being rendered by the videopresentation device is that known video content item, and the computingsystem could responsively take action as noted above.

Unfortunately, however, this process could be problematic forinteractive video content such as a video game. As explained above,interactive video content could vary dynamically based on userinteraction and could therefore differ each time it is rendered. As aresult, it could be impractical to establish reference fingerprint datathat would serve as a reliable point of comparison for identifying theinteractive video content being rendered at any given time.

On the other hand, as noted above, interactive video content such as avideo game may also contain some pre-established or statically-definedvideo segments, such as cutscenes or the like, and those pre-establishedvideo segments would not vary dynamically based on user interactionwhile they are being rendered. Further, the pre-established videosegments in a given video content item, such as a given video game, maybe unique to that video content item (e.g., not contained in other videocontent items). When addressing interactive video content, it couldtherefore be useful to focus the fingerprint comparison on suchpre-established video segments.

For instance, as the computing system obtains a digital fingerprint ofthe video content being rendered by the video presentation device, thecomputing system could compare that fingerprint with referencefingerprints of various pre-established video segments correspondingrespectively with particular video content items (such as particularvideo games). Upon determining that the fingerprint of the video contentbeing rendered by the video presentation device matches the referencefingerprint of a specific pre-established video segment, the computingsystem could thereby conclude that video content being rendered by thevideo presentation device is the video content item that the referencedata correlates with that specific pre-established video segment.

Once the computing system thereby determines the identity of the videocontent being rendered by the video presentation device, at issue maythen be whether, after the pre-established video segment ends, the videopresentation device continues to render the identified video content.For instance, with a video game, at issue may be whether the videocontent being rendered after an identified cutscene ends continues to bethe video game associated with that cutscene. Further, at issue could behow long the rendering of that identified video content continuesbetween instances of pre-established video segments, such as how longinteractive play of an identified video game continues between cutscenesor the like.

Here again, however, difficulty could arise due to the interactivenature of the video content. Namely, once a pre-established videosegment ends and the video content transitions to be interactive contentthat dynamically varies based on user interaction during the rendering,it could once again be difficult to or impractical to use merefingerprint matching as a basis to determine whether the video contentbeing rendered continues to be the identified video content.

To help address this difficulty, in accordance with the presentdisclosure, the computing system could make use of a neural network orother machine-learning algorithm that is trained based on many instancesof playout of the interactive video content at issue. Considering avideo game, for instance, a neural network could be trained based ondigital fingerprints of many instances of actual game play, such asdigital fingerprints of numerous online play-though videos or the like.Although the interactive video content of a given game may differ eachtime the game is played, there may be sufficient similarity betweeninstances of the game that a neural network could be trained torecognize the game and to classify or distinguish between the game andother games and/or at least between the game and non-game video content.

Thus, as a computing system obtains a digital fingerprint representingthe video content being rendered by the video presentation device, thecomputing system could apply a fingerprint matching process as describedabove and could thereby identify the video content based on afingerprint match as to a pre-established video segment associated withknown video content. And as the computing system continues to obtain thedigital fingerprint of the video content being rendered by the videopresentation device, the computing system could then apply a neuralnetwork as to at least a dynamically-defined portion of the videocontent and could thereby detect that the video content being renderedby the video presentation device continues to be the identified videocontent. Advantageously, the computing system could then responsivelytake action such as that as noted above.

Accordingly, in one respect, disclosed herein is a method of detectingand responding to rendering of video content by a video presentationdevice, where the video content includes (i) a pre-established videosegment that does not vary based on user-interaction during therendering and (ii) a dynamically-defined video segment that varies basedon user-interaction during the rendering.

In accordance with the method, a computing system obtains a queryfingerprint generated in real-time during the rendering as arepresentation of the video content being rendered, with the queryfingerprint including a first portion representing the pre-establishedvideo segment and a second portion representing the dynamically-definedvideo segment. Further, while obtaining the query fingerprint, thecomputing system (a) detects a match between the first portion of thequery fingerprint and a reference fingerprint that represents thepre-established video segment, (b) based on the detecting of the match,identifies the video content being rendered, (c) after identifying thevideo content being rendered, applies a trained neural network to atleast the second portion of the query fingerprint, and (d) detects,based on the applying of the neural network, that rendering of theidentified video content continues. And still further, responsive to atleast the detecting that rendering of the identified video contentcontinues, the computing system then takes action associated with theidentified video content.

In addition, in another respect, disclosed is a method of detecting andresponding to playing of a video game, where the video game is renderedin real-time on a video display unit, and wherein the video gameincludes (i) cutscene video segments that do not vary based onuser-interaction during the rendering and (ii) interactive videosegments that vary based on user-interaction during the rendering.

In accordance with this method, a computing system obtains a queryfingerprint generated in real-time during the rendering as arepresentation of the video game being played, with the queryfingerprint including (i) a first portion representing a first cutscenevideo segment and (ii) a second portion representing a first interactivevideo segment. Further, the computing system detects a match between thefirst portion of the query fingerprint and a reference fingerprint thatrepresents the first cutscene video segment and, based on the detectedmatch, identifies by the computing system the video game being rendered.And after identifying the video content being rendered, the computingsystem applies a trained neural network to at least the second portionof the query fingerprint to detect that the video content being renderedcontinues to be the identified game. And still further, responsive to atleast detecting that the video content being rendered continues to bethe identified video game, the computing system causes supplementalcontent to be presented.

Yet additionally, disclosed is a computing system including a networkcommunication interface, a processing unit, non-transitory data storage,and program instructions stored in the non-transitory data storage andexecutable by the processing unit to carry out operations for detectingand responding to rendering of video content by a video presentationdevice, where the video content includes, in order, (i) apre-established video segment that does not vary based onuser-interaction during the rendering and (ii) a dynamically-definedvideo segment that varies based on user-interaction during therendering.

The operations carried out by the computing system could be similar tothose noted above. For example, the operations could include receivingfrom a video presentation device, via the network communicationinterface, a query fingerprint generated in real-time during therendering as a representation of the video content being rendered, wherethe query fingerprint includes, in order, (i) a first portionrepresenting the pre-established video segment and (ii) a second portionrepresenting the dynamically-defined video segment. Further, theoperations could include detecting a match between the first portion ofthe query fingerprint and a reference fingerprint that represents thepre-established video segment and, based on the detected match,identifying the video content being rendered. And the operations couldinclude, after identifying the video content being rendered, applying amachine learning algorithm to at least the second portion of the queryfingerprint to detect that the video content being rendered continues tobe the identified video content. And still further, the operations couldinclude, responsive to at least detecting that the video content beingrendered continues to be the identified video content, causing a userdevice to render supplemental content.

These as well as other aspects, advantages, and alternatives will becomeapparent to those of ordinary skill in the art by reading the followingdetailed description, with reference where appropriate to theaccompanying drawings. Further, it should be understood that thedescriptions provided in this summary and below are intended toillustrate the invention by way of example only and not by way oflimitation.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified block diagram of an example system in whichvarious disclosed principles can be applied.

FIG. 2 is a simplified block diagram of an example network arrangementin which a video presentation device communicates with a networkplatform to facilitate implementing various disclosed principles.

FIG. 3 is a timing diagram illustrating an example of video contentincluding one or more dynamically-defined portions and one or morepre-established portions.

FIG. 4 is a diagram depicting operations that can be carried out inaccordance with the present disclosure.

FIG. 5 is another diagram depicting operations that can be carried outin accordance with the present disclosure.

FIG. 6 is a simplified block diagram of an example computing system.

FIG. 7 is a simplified block diagram of an example video presentationdevice.

DETAILED DESCRIPTION

Referring to the drawings, FIG. 1 is a simplified block diagram of anexample system in which various disclosed principles can be applied. Itwill be understood, however, that this and other arrangements andprocesses described herein can take various other forms. For instance,elements and operations can be re-ordered, distributed, replicated,combined, omitted, added, or otherwise modified. Further, it will beunderstood that functions described herein as being carried out by oneor more entities could be implemented by and/or on behalf of thoseentities, through hardware, firmware, and/or software, such as by one ormore processing units executing program instructions or the like.

As shown in FIG. 1, the example system includes a video presentationdevice 12 communicatively linked with one or more video sources 14 andconfigured to receive video content from the video source(s) and torender the video content for viewing by a user 16.

In a representative implementation, the video presentation device 12could be a computer monitor, television, or other device configured toreceive and render video content on a display or the like. As such, thevideo presentation device 12 could include one or more video input ports(e.g., HDMI, DVI, component video, composite video, VGA, and/or otherwired or wireless input ports) for receiving video content, a displaypanel (e.g., an OLED, LED, LCD, plasma, and/or other panel) forpresenting the video content, and one or more processing components(e.g., video processors) for rendering the video content as it arrivesvia a selected video input port and for outputting the rendered videocontent on the display for presentation to the user 16.

The video sources 14 could then include any of a variety of videocomponents configured to provide video content suitable for receipt andrendering by the video presentation device 12 and to receive and respondto input from user 16 for controlling video-content output, such as forinteractively defining the video content. As shown, the video sourcescould include one or more local video source components 18 and one ormore remote video source components 20, any of which could be configuredto generate and output, or to receive and forward, the video content forrendering by video presentation device 12. Further, at least one localvideo source component 18 could be situated in proximity to the user 16or otherwise in communication with the user 16 and equipped to receiveuser input for controlling video content output.

By way of example, the video sources 14 could include a video gameconsole connected locally by an HDMI cable or other wired or wirelessmechanism with the video presentation device 12. Such a console could bea specialized computer designed to facilitate interactive video gameplayby executing game software from DVDs, CDs, internal storage, networksources, or the like. As such, the console could receive user inputselecting, providing, or otherwise designating a video game to play Andas the console then executes the game and delivers associated videocontent to the video presentation device for rendering, the consolecould receive user input dynamically controlling how the game proceedsand thus dynamically defining the video content being delivered.

Alternatively, the video sources 14 could include a general purposecomputer (e.g., a desktop or portable computer) connected locally withthe video presentation device and could include a remote game server innetwork communication with the local computer. In this arrangement, thelocal computer could provide a user interface, perhaps a generic browserinterface, through which the user could interact with the game server,and the local computer could be configured to receive video content fromthe game server and to deliver the video content to the videopresentation device for rendering. Through the user interface of thelocal computer, the user could thus select or otherwise designate avideo game to play and could control how the game proceeds and thus whatvideo content the game server delivers for rendering by the videopresentation device.

Still alternatively, the video sources 14 could take other forms, notnecessarily limited to video game sources. For instance, the videosources could include a television tuner, such as a cable-TV orsatellite set top box, connected locally with the video presentationdevice and configured to receive user selection of a television channeland to responsively tune to that channel and deliver video content ofthe television channel to the video presentation device for rendering.And the video sources could include digital video recorders/players,which could similarly receive user selection of video content to playand could responsively deliver the selected video content to the videopresentation device for rendering Moreover, the video sources couldinclude an audio/video receiver or other such device that enables userselection of a video source to provide video content and that receivesand forwards video from the selected source to the video presentationdevice for rendering. Other examples are possible as well.

As noted above, as the video presentation device receives and rendersvideo content, the video presentation device may have no indication ofthe identity of that video content. Rather, the video presentationdevice may be configured simply to passively receive the video contentas a video stream from a video source and to render the received videocontent. Per the present disclosure, however, the video presentationdevice may be in communication with a network platform and may work withthe network platform to facilitate identification of the video contentbeing rendered and thus to facilitate useful content-specific action asnoted above. (Alternatively, features of the network platform could beprovided as part of the video presentation device or locally inassociation with the video presentation device.)

FIG. 2 illustrates an example network arrangement in which the videopresentation device 12 is in communication with a network platform 22via a network 24, such as the Internet. In practice, the videopresentation device 12 may sit as a node on a local area network (LAN)at customer premises, with the video presentation device having anassigned Internet Protocol (IP) address on the LAN and the LAN having anIP address on the Internet. Further, the network platform 22 maycomprise a server that is also be accessible at an IP address on theInternet.

With this arrangement, the video presentation device may initiate andengage in IP communication with the platform via the Internet to providethe platform with a digital fingerprint of video content in real-time asthe video content is being rendered, and the platform may continuallyevaluate the digital fingerprint as it arrives, in order to identify thevideo content and to responsively trigger content-specific action.

To facilitate this in practice, the video presentation device 12 oranother entity could be configured to generate a digital fingerprint ofthe video content that is being rendered by the video presentationdevice and to transmit the digital fingerprint to the platform 22 foranalysis.

For instance, as shown in FIG. 2, the video presentation device couldinclude a fingerprint generator 26, which could be configured togenerate a digital fingerprint of the video content that is beingrendered by the video presentation device. Such a fingerprint generatorcould be configured to generate the digital fingerprint of video contentas the video presentation device is receiving the video content and/oras the video presentation device is processing the video content forpresentation. As such, the fingerprint generator could receive as inputa copy of the video content arriving at the video presentation deviceand/or being processed for presentation by the video presentationdevice, and to apply any media fingerprinting process now known or laterdeveloped to generate a digital fingerprint of the video content.

Without limitation, an example digital fingerprinting process couldapply on a per video frame basis and could involve establishing arepresentation of luminosity and/or other video characteristics. Forinstance, for a given video frame, the fingerprint generator couldprogrammatically divide the frame into a grid, and the fingerprintgenerator could measure luminosity of the frame per grid cell andgenerate a bit string with each bit or series of bits representingluminosity of a respective grid cell, or representing a weighteddifference between the luminosity of certain defined pairs of the gridcells, or the like. Further, the fingerprint generator could apply thisprocess continually to generate the digital fingerprint over time as asequence of fingerprints (e.g., as a fingerprint stream). For instance,the fingerprint generator could apply this process to each frame, toeach key frame, periodically, or on another defined basis, with eachframe's bit string defining a digital fingerprint and/or with aspecified hash, combination or series of such bit strings or otherrepresentative values defining a digital fingerprint, on a slidingwindow basis. Other digital fingerprinting processes could be used aswell.

In practice, the video presentation device 12 could be configured toprogrammatically establish a communication session (e.g., a TCP socket)with the platform 22 and to transmit to the platform in that session thedigital fingerprint of the video content being rendered (referred toherein as a “query fingerprint”). For instance, the video presentationdevice could be configured to periodically or from time to time transmitto the platform a message carrying the digital fingerprint of a latestframe, series of frames, or other portion of the video content beingrendered by the video presentation device. And the platform couldthereby receive the digital fingerprint for analysis, largely inreal-time as the video content is being rendered by the videopresentation device.

Alternatively, the video presentation device could transmit to theplatform, and the platform could thus receive, various data regardingthe video content being rendered by the video presentation device, on anongoing basis or other basis, to enable the platform itself or anotherentity to generate a query fingerprint of the video content beingrendered by the video presentation device. For example, the videopresentation device could transmit to the platform portions of the videocontent being rendered by the video presentation device, such asindividual frames (e.g., snapshots) or other segments of the videocontent. And the platform could apply a fingerprint generator togenerate a digital fingerprint of the video content for analysis.

In line with the discussion above, the platform in this arrangementcould evaluate the query fingerprint of the video content being renderedby the video presentation device, so as to identify the video contentthat is being rendered and to responsively take content-specific action.

As explained above, this process could address a scenario where thevideo content being rendered includes a combination of pre-establishedvideo content and interactive video content. For instance, the processcould address a scenario where the video content being rendered is avideo game that includes (i) one or more pre-established video segmentssuch as cutscenes and the like that do not vary based onuser-interaction while being rendered and (ii) one or moredynamically-defined video segments, such as ongoing game-play videocontent, that vary based on user-interaction while being rendered.

FIG. 3 is a timing diagram illustrating an example of how such videocontent could be structured. As shown in FIG. 3, the video contentincludes alternating dynamically defined and pre-established videosegments. In particular, the video content includes a firstdynamically-defined video segment 30 from time T₁ to time T₂, a firstpre-established video segment 32 from time T₂ to time T₃, a seconddynamically-defined video segment 34 from time T₂ to time T₃, and asecond pre-established video segment 36 from time T₃ to time T₄.

If this example video content is of a particular video game, forinstance, the first and second dynamically-defined video segments 30, 34could be interactive video content of respective levels of game play,where the video content is defined dynamically based on user interactionwhile playing (e.g., based on choices the user makes during game play).And the first and second pre-established video segments 32, 36 could bestatically defined cinematic cutscenes that are specific to the videogame and are presented to the user as the user transitions betweenlevels of game play, or could be other pre-established video segmentsspecific to the video game.

In line with the discussion above, the network platform 22 could beconfigured to apply a fingerprint matching process in order to identifythe video content being rendered, by detecting that the fingerprint ofthe video content being rendered matches a reference fingerprint of apre-established video segment that is known to correspond with aparticular video content item (e.g., a particular video game). Further,the platform could be configured to apply a trained neural network inorder to determine that the video content being rendered continues to bethe identified video content, as a basis to trigger associated action.

In an example implementation, the platform could include separate butinterworking servers or other modules as shown in FIG. 2. Namely, theplatform could include a proxy server 38 having an outwardly facing IPaddress for communicating with the video presentation device, afingerprint-matching server 40 for conducting the fingerprint-matchingprocess to identify the video content being rendered, and aneural-network server 42 for applying a trained neural network todetermine that the video content being rendered continues to be theidentified video content, to facilitate triggering action based on thecontinued presentation of that video content. These servers could sit asnotes on a LAN or could otherwise be communicatively linked together.

With this arrangement, the proxy server 38 could receive from the videopresentation device the query fingerprint of the video content beingrendered by the video presentation device and, as the query fingerprintarrives, could forward the query fingerprint to the fingerprint-matchingserver 40 for analysis.

As the fingerprint-matching server 40 receives the query fingerprint,the fingerprint-matching server could then continually (e.g., with quickperiodicity) compare the query fingerprint with reference fingerprintsof pre-established video segments each known to appear in a respectivevideo content item, in search of a fingerprint match. And upon detectingsuch a match, the fingerprint-matching server 40 could conclude that thevideo content being rendered includes the pre-established video segmentwhose fingerprint matched, and could therefore identify the videocontent being rendered as being the video content item that is known toinclude that pre-established video segment. For instance, by detecting afingerprint match as to a cutscene known to appear in a particular videogame, the fingerprint-matching server could conclude that the videocontent being rendered is that particular video game.

To compare the query fingerprint stream with a reference fingerprint,the server could compare corresponding portions of the fingerprints witheach other to determine whether the portions match exactly or withindefined tolerances. For example, on a per frame basis or at anotherdesired rate, the server could compute a maximum deviation between thefingerprints and determine if the maximum deviation is within apredefined tolerance. Further, if the fingerprints are binary, thiscould be a Boolean determination or could involve computing a Hammingdistance (as a count of mismatches between respective bit locations inthe fingerprints), and if the fingerprints are more complex values, suchas decimal values or vectors (e.g., grey values per video frame region),this could involve determining a distance between the values or vectors.Numerous other examples are possible as well.

Once the fingerprint-matching server detects the start of a fingerprintmatch as to a pre-established video segment and thus identifies thevideo content being rendered, the fingerprint-matching server couldsignal to the proxy server 38 to indicate the determined identity of thevideo content (e.g., the name of the video game being rendered), perhapsalong with a unique identifier of the detected pre-established videosegment (e.g., a cutscene identifier). Further, the fingerprint-matchingserver could then continue to compare the incoming query fingerprintwith the reference fingerprint data to determine when the detectedfingerprint-match as to that pre-established video segment ends, andthen to search for a fingerprint match as to a next pre-establishedvideo segment, and so forth.

When the fingerprint-matching server determines from its fingerprintanalysis that a detected fingerprint match has ended, that could signifythat the video presentation device has finished rendering the associatedpre-established video segment. At issue at that point may then bewhether the video presentation device continues to render the identifiedvideo content and perhaps for how long. For instance, if the identifiedvideo content is a particular video game and the fingerprint-matchingserver detects an end of a fingerprint match as to a cutscene of thatvideo game, at issue may then be whether the video presentation devicecontinues to render the identified video game (as opposed to renderingsome other video content) and perhaps how long the rendering of theidentified video game continues before the occurrence of a nextcutscene.

As explained above, this issue could be difficult to resolve where thecontinued video content is not another pre-established video segment butis rather a dynamically-defined video segment such as interactive usergame play, which may vary greatly depending on user interaction.

To address this issue, the fingerprint-matching server could signal tothe proxy server to indicate that the fingerprint-matching server hasdetected the end of a cutscene, and the proxy server could thenresponsively invoke the assistance of neural-network server 42. Namely,as the proxy server continues to receive the query fingerprint of thevideo content being rendered by the video presentation device, the proxyserver could forward that query fingerprint to the neural-network serverand could direct the neural-network server to begin classifying thequery fingerprint in a manner that helps indicate whether the videocontent being rendered by the video presentation device continues to bethe identified video content.

In response to this signal from the proxy server, the neural networkserver could feed the arriving query fingerprint through a neuralnetwork that has been trained to classify digital fingerprints with adesired level of granularity. (Alternatively, the neural network servercould regularly receive the arriving query fingerprint from the proxyserver and could apply this analysis in parallel with thefingerprint-matching server's analysis, to help identify the videocontent being rendered.)

By way of example, if the identified video content is a particular videogame, the neural-network server could apply a neural network that istrained to distinguish between video game content and non-video-gamecontent. For instance, the neural network could be trained based oninput data that includes (i) many digital fingerprints ofdynamically-defined video game content and (ii) many digitalfingerprints of television content or other non-video-game content.Through this training, the neural network could learn video contentcharacteristics that are indicative of video game content and videocontent characteristics that are indicative of non-video-game content,and the neural network could thus learn to distinguish video gamecontent from non-video-game content.

Applying such a trained neural network to the query fingerprintrepresenting the video content being rendered by the video presentationdevice, the neural-network server could thus determine whether the videocontent being rendered by the video presentation device continues tovideo game content or not and could signal to the proxy serveraccordingly.

If the neural network thereby determines that the video content beingrendered by the video presentation device is a video game, then areasonable conclusion given the identification of the video content asbeing a particular video game is that the video game being rendered bythe video presentation device continues to be that identified videogame. Therefore, a reasonable conclusion at this point is that the useris continuing to play the identified video game. Whereas, if and whenthe neural network determines that the video content being rendered bythe video presentation device is not a video game, then a reasonableconclusion is that the video content being rendered by the videopresentation device is no longer the identified video game, andtherefore that the user has stopped playing the identified video game.

Alternatively or additionally, the neural-network server could apply aneural network that is trained to distinguish more granularly betweenparticular video games and/or between other sorts of interactive videocontent. For instance, the neural network could be trained based oninput data that includes, separately and respectively for each ofvarious particular video games, many digital fingerprints of dynamicallydefined video game content from instances of playing the particularvideo game. Through that training, the neural network could learn videocontent characteristics that are specific to particular video games, andthe neural network could thus learn to distinguish one video game fromanother.

In that case, based on a neural network analysis of the queryfingerprint representing the video content being rendered by the videopresentation device, the neural-network server could determine whetherthe video content being rendered by the video presentation devicecontinues to be the particular identified video game, as compared withanother video game or other interactive video content for instance. Andthe neural-network server could accordingly signal to the proxy server,to indicate whether the video content being rendered by the videopresentation device continues to be the identified video content.

With this more granular neural-network implementation, each of variousvideo content items (e.g., particular video games) could be designatedby a name or other identifier, and the neural network could referencethat identifier in its classification. Once the proxy server learns fromthe fingerprint-matching server the identity of the video content beingrendered by the video presentation device, the proxy server could theninform the neural-network server of the video content identifier, andthe neural-network server could responsively apply its neural network todetermine whether the video content with that identifier is the videocontent that continues to be rendered by the video presentation device,and could report back to the proxy server accordingly.

The neural network applied by the neural-network server in this processcould take any of a variety of forms. By way of example, the neuralnetwork could be a recurrent deep neural network that uses a LongShort-Term Memory (LSTM) architecture, the operation of which isdescribed in Hochreiter et al., “Long Short-Term Memory,” NeuralComputation 9(8): 1735-1780, 1997,http://deeplearning.cs.cmu.edu/pdfs/Hochreiter97_lstm.pdf.Alternatively, other forms of neural networks (e.g., gated recurrentunit neural networks, convolutional neural networks, and others nowknown or later developed) could be applied as well.

FIG. 3 illustrates how this process could play out with an incomingdigital fingerprint representing example video segments 30-36. Here, thenetwork platform 22 would receive a query fingerprint that representsthe video content being rendered, and thus the query fingerprint couldinclude, in order, a first portion representing dynamically-definedvideo segment 30, a second portion representing pre-established videosegment 32, a third portion representing dynamically-defined videosegment 34, and a fourth portion representing pre-established videosegment 36.

In line with the discussion above, as the platform receives this queryfingerprint, the proxy server could forward the query fingerprint to thefingerprint-matching server, and the fingerprint-matching server couldcontinually compare the query fingerprint with reference fingerprintsrepresenting various pre-established video segments corresponding withknown video content items. As a result, shortly after time T₂ (at timeT_(A)), the fingerprint-matching server could detect a match with areference fingerprint of a pre-established video segment correspondingwith a particular known video game, thus supporting a conclusion thatthe video content being rendered is that particular video game. And thefingerprint-matching server could report this finding to the proxyserver.

As the fingerprint-matching server then continues to evaluate theincoming query fingerprint, shortly after time T₃ (at time T_(B)), thefingerprint-matching server could then detect a mismatch, resulting fromthe pre-established video segment ending, and could report this to theproxy server. In response to this mismatch and thus to the ending of thepre-established video segment, the proxy server could then beginforwarding the incoming digital fingerprint to the neural-network server(if the proxy server was not doing so already) and could signal to theneural-network server the determined identity of the video-content atissue.

In response, the neural-network server could then apply a trained neuralnetwork to the query fingerprint as the query fingerprint arrives, in aneffort to classify the query fingerprint and thus to determine whetherthe query fingerprint continues to represent the identified videocontent. For instance, if the identified video content is a particularvideo game, then the neural-network server could continually apply aneural network to the arriving query fingerprint to determine whetherthe query fingerprint continues to represent video-game contentgenerally and/or to determine whether the fingerprint continues torepresent the specifically identified video game. And the neural-networkserver could report its findings to the proxy server, also perhapscontinually.

Meanwhile, as the fingerprint-matching server continues to compare theincoming query fingerprint with reference fingerprints, shortly aftertime T₄ (at time T_(C)), the fingerprint-matching server could detect amatch with a reference fingerprint of another pre-established videosegment that also corresponds with the identified video game and couldreport that finding to the proxy server. As this match with apre-established video segment indicates an end of thedynamically-defined video segment 34, the proxy server could thendiscontinue forwarding the digital fingerprint to the neural-networkserver and could signal to the neural-network server to stop applicationof the neural-network.

This process could then continue iteratively, with thefingerprint-matching server again detecting an end of the match with thefingerprint of the detected pre-established video segment andresponsively signaling to the proxy server, the proxy serverresponsively signaling to the neural-network server, and the neuralnetwork server responsively applying a neural network to determinewhether the video content being rendered by the video presentationdevice continues to be the identified video content.

As noted above, this process assumes that the fingerprint-matchingserver has access to reference fingerprints of various pre-establishedvideo segments each corresponding with known video content items, andthat the neural-network server is configured with a neural network thatis trained based on various known video content items. To facilitatethis process in practice, the network platform 22 could further includesa provisioning server 44 that could interwork with thefingerprint-matching server 40 and neural-network server 42 to helpestablish the reference fingerprints and train the neural network.

As to video games, the provisioning server could obtain numerous videorecordings of actual instances of video-game play and could generatedigital fingerprints of those video recordings for use to generate thereference fingerprints of pre-established video segments (e.g.,cutscenes and the like) and for use to train one or more neuralnetworks.

The provisioning server could obtain these recordings of actualinstances of video-game play in various ways. One useful source of therecordings, for instance, is online “Let's Play” (LP) videos and otherplay-through videos, commonly accessible on websites such as Twitch andYouTube. These play-through videos are recordings of actual instances ofgame play, sometimes edited with scripted narration, and sometimes beingmore raw recordings of game play captured on the fly.

In an example implementation, the provisioning server could beconfigured to automatically search for and generate digital fingerprintsof these play-through videos. By way of example, an administrator of thenetwork platform could enter into the provisioning server names ofvarious known video games, and the provisioning server couldautomatically search websites such as Twitch and YouTube forplay-through videos of the named video games. As the provisioning serverfinds such videos, the provisioning server could then automatically playthe videos (e.g., receive streaming video representations of the videos)and, applying a fingerprint generator such as that noted above, generatecorresponding digital fingerprints of the videos. The provisioningserver could then save the resulting digital fingerprints in correlationwith the names of the video games.

Provided with these digital fingerprints of actual instances of gameplay, the provisioning server could then programmatically evaluate thedigital fingerprints to identify fingerprint segments that representpre-established video segments. For instance, by evaluating fingerprintsof multiple instances of play of a particular video game, theprovisioning server could identify a fingerprint segment that repeatedlyoccurs within each instance of play of that video game or that occurs atleast once in each instance of play of that video game. Given the staticnature of pre-established video segments such as cutscenes, theprovisioning server could thus deem such recurring fingerprint segmentsto represent pre-established video segments of the video game. Theprovisioning server could therefore store those fingerprint segments asreference fingerprints representing pre-established video segments incorrelation with the known identity of the video game at issue, andperhaps with an identifier of the pre-established video segment. And theprovisioning server could make that reference fingerprint data availableto the fingerprint-matching server for use to identify video contentbeing rendered by a video presentation device as discussed above.

Alternatively, the provisioning server could obtain referencefingerprints of pre-established video segments correlated with knownvideo content items in other ways. For instance, a person could watchvideo recordings of various video games and could manually identify thepre-established video segments (e.g., by their start and stop times) anddirect the provisioning server to generate reference fingerprints ofthose identified video segments. Other examples are possible as well.

Further, the provisioning server could provide the digital fingerprintsof the actual instances of video game play to the neural-network serverfor use by the neural-network server to train one or moreneural-networks. The provisioning server could provide theneural-network server with the full digital fingerprints of each suchrecording, along with the video game identity of each recording. Or theprovisioning server could separate out the fingerprint segments thatrepresent the dynamically-defined segments of the recordings, based onthose fingerprint segments not being the segments that were deemed torepresent pre-established video segments, and the provisioning servercould provide those fingerprint segments to the neural-network serveralong with the video game identity of each recording.

In addition, to enable the neural-network server to train a neuralnetwork to distinguish between video-game content and non-video-gamecontent, the provisioning server could also obtain digital fingerprintsof non-video-game content, such as television content. For instance, theprovisioning server and/or other associated servers could include one ormore television watching stations having tuners for receiving variouschannels of television content, and could use a fingerprint generatorlike that described above to generate digital fingerprints of thattelevision content. The provisioning server could then provide thesedigital fingerprints of non-video-game content to the neural-networkserver for use to train one or more neural networks as described above.

In accordance with the present disclosure, as noted above, the networkplatform could be configured to take actions in response to determiningthe identity of the video content being rendered by the videopresentation device, and perhaps specifically in response to detecting athreshold duration of the video presentation device continuing to renderan identified video content item.

By way of example, once the proxy server learns the identity of thevideo content being rendered by the video presentation device (e.g., theidentity of a particular video game being rendered by the videopresentation device), the proxy server or an associated entity couldrecord ratings-data regarding presentation of that video content. Forinstance, the proxy server could record the fact that the videopresentation device is presenting the identified video content, such asby adding to a count or other statistic of the identified video contentbeing presented, as data to indicate the extent to which that videocontent gets presented. Further, the proxy server could record such dataper video presentation device (as device-specific viewing analytics) andassociated demographics.

As another example, once the proxy server learns the identity of thevideo content being rendered by the video presentation device, the proxyserver or an associated entity could cause the video presentation deviceor another user device to present supplemental content, perhaps contentassociated with the identified video content. For instance, the proxyserver could cause the video presentation device or another user deviceto present a pop-up advertisement offering virtual content or physicalmerchandise that could be of interest given that the identified videocontent (e.g., additional game levels or other game content), oroffering help or other information of possible interest. In particular,the proxy server could send to the video presentation device or otheruser device a message carrying such supplemental content with adirective to which the video presentation device or other user devicewill respond by superimposing the supplemental content over thepresented video content (e.g., at a corner or edge of the display) forviewing by a user or otherwise presenting the supplemental content to auser.

As a specific example of this, as noted above, the proxy server coulddetermine how long the video presentation device continues to render theidentified video content between instances of pre-established videosegments of the video content and could take action based on thatdetermined duration. For instance, the proxy server could determine howlong an interactive video segment of an identified video game continuesbetween cutscenes of that video game, possibly indicating how long auser has been trying to reach a next level of game play, and could takeaction based on that duration. If the determined duration is thresholdlong (and still ongoing), as shown in FIG. 3 at time T_(D) for instance,then the proxy server could responsively cause the video presentationdevice to present the user with help content, such as game-play hints,on grounds that the user appears to be struggling. Whereas, if thedetermined duration is threshold short, then the proxy server couldresponsively cause the video presentation device to present the userwith offers to purchase of higher game levels or the like, on groundsthat the user appears to be an expert player.

This evaluation of duration between pre-established video segments couldbe specific to the identified video content, with one or more durationthresholds established based on an historical statistical analysis ofactual instances of playing the identified video content. In practice,the provisioning server or another entity could develop such thresholds.For example, as to a particular video game, the provisioning servercould evaluate the duration between particular cutscenes in that videogame, in each of many instances of play of that video game (e.g.,play-through recordings or the like) and could average those durationsor otherwise roll up the duration data to establish what might be deemeda typical duration between the cutscenes. And the provisioning servercould provide the proxy server with data indicating those durations(along with identifiers of the cutscenes), for use as thresholds todetermine whether the duration between those cutscenes in a giveninstance of play of the video game is threshold long or threshold short.Other implementations are possible as well.

FIG. 4 is next a diagram of an example method in line with thediscussion above, to detect and respond to rendering of video content bya video presentation device, where the video content includes (i) apre-established video segment that does not vary based onuser-interaction during the rendering and (ii) a dynamically-definedvideo segment that varies based on user-interaction during therendering.

As shown in FIG. 4, at block 40, the method includes a computing systemobtaining (e.g., receiving or establishing) a query fingerprintgenerated in real-time during the rendering as a representation of thevideo content being rendered, where the query fingerprint includes afirst portion representing the pre-established video segment and asecond portion representing the dynamically-defined video segment.Further, at block 42, shown concurrent with block 40, the computingsystem detects a match between the first portion of the queryfingerprint and a reference fingerprint that represents thepre-established video segment, (b) based on the detecting of the match,identifies the video content being rendered, (c) after identifying thevideo content being rendered, applies a trained neural network to atleast the second portion of the query fingerprint, and (d) detects,based on the applying of the neural network, that rendering of theidentified video content continues. In turn, at block 44, responsive toat least the detecting that rendering of the identified video contentcontinues, the computing system takes action specific to the identifiedvideo content.

In line with the discussion above, the video content in this methodcould comprise video game content, and the act of identifying the videocontent being rendered could comprise determining an identity of aparticular video game being rendered. Further, the act of detecting thatrendering of the identified video content continues could comprisedetecting that the video content being rendered by the videopresentation device continues to be video game content and/or detectingthat the video content being rendered continued to be the particularidentified video game. And still further, the act of taking actionspecific to the identified video content could comprise causingpresentation of supplemental content comprising an offer for additionalvideo game content.

As further discussed above, the method could additionally include thecomputing system detecting an ending of the detected match (as to thepre-established video segment), in which case the act of applying thetrained neural network could be responsive to at least the detecting ofthe ending of the detected match.

In addition, the method could include the computing system determining,based on the applying of the trained neural network to at least thesecond portion of the query fingerprint, that rendering of thedynamically-defined portion of the video content has continued for atleast a threshold duration. And the act of taking action specific to theidentified video content could comprise causing a presentation ofsupplemental content. Moreover, the act of presenting supplementalcontent could be further responsive to the act of determining thatrendering of the dynamically-defined portion of the video content hascontinued for at least the threshold duration. For instance, thedynamically-defined portion of the video content could compriseinteractive video game content, and the act of causing presentation ofthe supplemental content further responsive to the dynamically-definedportion of the video content having continued for at least the thresholdduration could involve causing presentation of video-game help content.

Further, the pre-established video segment could be labeled a firstpre-established video segment, and the video content could include asecond pre-established video segment that also does not vary based onuser-interaction during the rendering, with the query fingerprintincluding a third portion that represents the second pre-establishedvideo segment. And in that case, the act of determining, based on theapplying of the trained neural network to at least the second portion ofthe query fingerprint, that rendering of the dynamically-defined portionof the video content has continued for at least the threshold durationcould comprise detecting a threshold long duration from rendering thefirst pre-established video segment until (e.g., before or as of)rendering the second pre-established video segment.

Moreover, the match could be labeled a first match, and the method couldadditionally include the computing system detecting a second matchbetween the third portion of the query fingerprint and a referencefingerprint that represents the second pre-established video segmentand, based on the detecting of the second match, discontinuing theapplying of the trained neural network to the query fingerprint.

In addition, in line with the discussion above, the computing systemcommunicate with the video presentation device via a network, and theact of the computing system obtaining the query fingerprint generated inreal-time during the rendering as a representation of the video contentbeing rendered could comprise the computing system receiving from thevideo presentation device, via the network, transmissions (e.g., acontinual transmission or sequential transmissions) of the queryfingerprint generated in real-time by the video presentation deviceduring the rendering of the video content.

Further, as discussed above, the method could additionally includeestablishing a set of reference data, including the referencefingerprint and the trained neural network, based on computerizedanalysis of various instances of video game play. For instance, themethod could involve automatically searching for and downloading (e.g.,receiving streaming playout) from a public packet-switched network atleast some of the various instances of video game play, such asplay-through videos, and conducting the computerized analysis on thedownloaded (e.g., streaming) instances of video game play.

FIG. 5 is next another diagram depicting a method in line with thediscussion above, for detecting and responding to playing of a videogame, where the video game is rendered in real-time on a video displayunit (e.g., a video presentation device or associated unit), and wherethe video game includes (i) cutscene video segments that do not varybased on user-interaction during the rendering and (ii) interactivevideo segments that vary based on user-interaction during the rendering.

As shown in FIG. 5, at block 50, the method includes a computing systemobtaining a query fingerprint generated in real-time during therendering as a representation of the video game being played, whereinthe query fingerprint includes (i) a first portion representing a firstcutscene video segment and (ii) a second portion representing a firstinteractive video segment. And the method includes subsequent blocksthat could be carried out while so obtaining the query fingerprint. Inparticular, at block 52, the method includes the computing systemdetecting a match between the first portion of the query fingerprint anda reference fingerprint that represents the first cutscene video segmentand, based on the detected match, identifying by the computing systemthe video game being rendered. At block 54, the method includes, afterso identifying the video content being rendered, the computing systemapplying a trained neural network to at least the second portion of thequery fingerprint to detect that the video content being renderedcontinues to be the identified game. And at block 56, the methodincludes, responsive to at least detecting that the video content beingrendered continues to be the identified video game, the computing systemcausing a presentation of supplemental content.

As further discussed above, this method could additionally include thecomputing system detecting an ending of the detected match, and theapplying of the trained neural network could responsive to at least thedetecting of the ending of the detected match. Further, the method couldadditionally include the computing system determining, based on theapplying of the trained neural network to at least the second portion ofthe query fingerprint, that the first interactive video segment hascontinued for at least a threshold duration, the presentation ofsupplemental content could be further responsive to the determining thatthe interactive portion of the video content has continued for at leastthe threshold duration, and the supplemental content could comprisevideo-game help content.

FIG. 6 is next a simplified block diagram of an example computing systemoperable in accordance with the present disclosure. This computingsystem could be embodied as the network platform 22 discussed aboveand/or as one or more other entities (possibly including the videopresentation device). As shown in FIG. 6, the example system includes anetwork communication interface 60, a processing unit 62, non-transitorydata storage 64, any or all of which could be integrated together or, asshown, communicatively linked together by a system bus, network, orother connection mechanism 66.

Network communication interface 60 could comprise one or more physicalnetwork connection mechanisms to facilitate communication on a networksuch as network 24 discussed above, and/or for engaging in direct ornetworked communication with one or more other local or remote entities.As such, the network communication interface could comprise a wirelessor wired Ethernet interface or other type of network interface, forengaging in IP communication and/or other type of network communication.

Processing unit 62, could then comprise one or more general purposeprocessors (e.g., microprocessors) and/or one or more specializedprocessors (e.g., application specific integrated circuits). Andnon-transitory data storage 64 could comprise one or more volatileand/or non-volatile storage components, such as optical, magnetic, orflash storage.

As shown, data storage 64 then stores program instructions 68, whichcould be executable by processing unit 62 to carry out variousoperations described herein, for detecting and responding to renderingof video content by a video presentation device, where the video contentincludes, in order, (i) a pre-established video segment that does notvary based on user-interaction during the rendering and (ii) adynamically-defined video segment that varies based on user-interactionduring the rendering.

As discussed above, for instance, the operations could then includereceiving from the video presentation device, via the networkcommunication interface, a query fingerprint generated in real-timeduring the rendering as a representation of the video content beingrendered, where the query fingerprint includes, in order, (i) a firstportion representing the pre-established video segment and (ii) a secondportion representing the dynamically-defined video segment. Further, theoperations could include detecting a match between the first portion ofthe query fingerprint and a reference fingerprint that represents thepre-established video segment and, based on the detected match,identifying the video content being rendered. The operations could theninclude, after identifying the video content being rendered, applying atrained neural network to at least the second portion of the queryfingerprint to detect that the video content being rendered continues tobe the identified video content, and responsive to at least detectingthat the video content being rendered continues to be the identifiedvideo content, causing a user device to render supplemental content.

Various features described above could be applied in this context aswell. For example, the video content could comprise video game content,the pre-established video segment could comprise a cutscene videosegment, and the dynamically-defined video segment could comprise aninteractive gameplay video segment. And in that case, the operationscould additionally comprise determining, based on the applying of thetrained neural network to at least the second portion of the queryfingerprint, that the interactive gameplay video segment has continuedfor at least a threshold duration, the causing of the user device torender the supplemental content could be further responsive to thedetermining that the interactive gameplay video segment has continuedfor at least the threshold duration, and the supplemental content couldcomprise video-game help content.

Finally, FIG. 7 is a simplified block diagram of an example videopresentation device operable in accordance with the present disclosure.In line with the discussion above, this video presentation device couldtake various forms. For instance, it could be a television, computermonitor, or other device that operates to receive and render videocontent.

As shown in FIG. 7, the example video presentation device includes avideo input interface 70, a video presentation interface 72, a networkcommunication interface 74, a processing unit 76, and non-transitorydata storage 78, any or all of which could be integrated together or, asshown, communicatively linked together by a system bus, network, orother connection mechanism 80.

Video input interface 70 could comprise a physical communicationinterface for receiving video content to be presented by the videopresentation device. As such, the media input interface could includeone or more wired and/or wireless interfaces for establishingcommunication with and receiving video content in analog or digital formfrom a video source. For example, the video input interface could one ormore of the interfaces noted above, among other possibilities.

Video presentation interface 72 could then comprise one or morecomponents to facilitate presentation of the received video content. Byway of example, the video presentation interface could comprise adisplay panel as well as one or more video display drivers or othercomponents for processing the received video content to facilitatepresentation of the video content on the display panel.

Network communication interface 74 could comprise a physical networkconnection mechanism to facilitate communication on a network such asnetwork 24 discussed above, and/or for engaging in direct or networkedcommunication with one or more other local or remote entities. As such,the network communication interface could comprise a wireless or wiredEthernet interface or other type of network interface, for engaging inIP communication and/or other type of network communication.

Processing unit 76 could then comprise one or more general purposeprocessors (e.g., microprocessors) and/or one or more specializedprocessors (e.g., application specific integrated circuits). Andnon-transitory data storage 78 could comprise one or more volatileand/or non-volatile storage components, such as optical, magnetic, orflash storage. Further, as shown, data storage 78 stores programinstructions 82, which could be executable by processing unit 76 tocarry out various operations described here. For example, the programinstructions could be executable to generate on an ongoing basis afingerprint of video content being rendered by the video presentationdevice, based on analysis of the media content being received at thevideo input interface 70 and/or being processed at the videopresentation interface, and to provide the generated fingerprint on anongoing basis to facilitate channel identification as described herein.

Note that, while the above discussion provides for using a neuralnetwork to determine that the video content being rendered by the videopresentation device continues to be the identified video content, othersorts of machine learning algorithms could be used for this purpose aswell. For example, a template-matching process could be used. Templatematching could involve identifying a sequence or other pattern of videoframes (possibly non-contiguous) specific a given video content item. Atemplate-matching server could thus apply a training process in which itevaluates reference fingerprints of interactive video content toidentify one or more such patterns per video content item. And thetemplate-matching server could then classify an incoming queryfingerprint by detecting that the query fingerprint includes a patternthat the training process had associated with a particular video contentitem. Other machine-learning processes could be used as well.

Exemplary embodiments have been described above. Those skilled in theart will understand, however, that changes and modifications may be madeto these embodiments without departing from the true scope and spirit ofthe invention.

We claim:
 1. Non-transitory data storage storing program instructionsexecutable by one or more processors to carry out operations to detectand respond to rendering of video content by a video presentationdevice, wherein the video content includes (i) a pre-established videosegment that does not vary based on user-interaction during therendering and (ii) a dynamically-defined video segment that varies basedon user-interaction during the rendering, the operations comprising:obtaining a query fingerprint generated in real-time during therendering as a representation of the video content being rendered, thequery fingerprint including a first portion representing thepre-established video segment and a second portion representing thedynamically-defined video segment; while obtaining the queryfingerprint, (a) detecting a match between the first portion of thequery fingerprint and a reference fingerprint that represents thepre-established video segment, (b) based on the detecting of the match,identifying the video content being rendered, (c) after identifying thevideo content being rendered, applying a trained neural network to atleast the second portion of the query fingerprint, and (d) detecting,based on the applying of the neural network, that rendering of theidentified video content continues; and responsive to at least thedetecting that rendering of the identified video content continues,taking action specific to the identified video content.
 2. Thenon-transitory data storage of claim 1, wherein the video contentcomprises video game content.
 3. The non-transitory data storage ofclaim 2, wherein detecting that rendering of the identified videocontent continues comprises detecting that the video content beingrendered by the video presentation device continues to be video gamecontent.
 4. The non-transitory data storage of claim 2, whereinidentifying the video content being rendered comprises determining anidentity of a particular video game being rendered, and whereindetecting that rendering of the identified video content continuescomprises detecting that the video content being rendered continues tobe the particular video game.
 5. The non-transitory data storage ofclaim 2, wherein taking action specific to the identified video contentcomprises causing presentation of supplemental content comprising anoffer for additional video game content.
 6. The non-transitory datastorage of claim 1, wherein the operations further comprise: detectingan ending of the detected match, wherein applying the trained neuralnetwork is responsive to at least the detecting of the ending of thedetected match.
 7. The non-transitory data storage of claim 1,whereinthe operations further comprise: determining, based on the applying ofthe trained neural network to at least the second portion of the queryfingerprint, that rendering of the dynamically-defined portion of thevideo content has continued for at least a threshold duration, whereintaking action specific to the identified video content comprises causinga presentation of supplemental content, and wherein causing thepresentation of supplemental content is further responsive to thedetermining that rendering of the dynamically-defined portion of thevideo content has continued for at least the threshold duration.
 8. Thenon-transitory data storage of claim 7, wherein the dynamically-definedportion of the video content comprises interactive video game content,and wherein causing presentation of the supplemental content furtherresponsive to the dynamically-defined portion of the video contenthaving continued for at least the threshold duration comprises causingpresentation of video-game help content.
 9. The non-transitory datastorage of claim 7, wherein the pre-established video segment is a firstpre-established video segment, wherein the video content includes asecond pre-established video segment that also does not vary based onuser-interaction during the rendering, and wherein the query fingerprintincludes a third portion representing the second pre-established videosegment, and wherein determining, based on the applying of the trainedneural network to at least the second portion of the query fingerprint,that rendering of the dynamically-defined portion of the video contenthas continued for at least the threshold duration comprises detecting athreshold long duration from rendering the first pre-established videosegment until rendering the second pre-established video segment. 10.The non-transitory data storage of claim 9, wherein the match is a firstmatch, the operations further comprising: detecting a second matchbetween the third portion of the query fingerprint and a referencefingerprint that represents the second pre-established video segmentand, based on the detecting of the second match, discontinuing theapplying of the trained neural network to the query fingerprint.
 11. Thenon-transitory data storage of claim 1, wherein obtaining the queryfingerprint generated in real-time during the rendering as arepresentation of the video content being rendered comprises receivingfrom the video presentation device, via a network, transmissions of thequery fingerprint generated in real-time by the video presentationdevice during the rendering of the video content.
 12. The non-transitorydata storage of claim 1, wherein the operations further comprise:establishing a set of reference data, including the referencefingerprint and the trained neural network, based on computerizedanalysis of various instances of video game play.
 13. The non-transitorydata storage of claim 12, wherein the operations further comprise:automatically searching for and downloading from a publicpacket-switched network at least some of the various instances of videogame play; and conducting the computerized analysis on the downloadedvarious instances of video game play.
 14. The non-transitory datastorage of claim 13, wherein the various instances of video game playcomprise play-through videos.
 15. The non-transitory data storage ofclaim 1, wherein the neural network comprises a Long Short Term Memoryneural network.
 16. Non-transitory data storage storing programinstructions executable by one or more processors to carry outoperations to detect and respond to playing of a video game, wherein thevideo game is rendered in real-time on a video display unit, and whereinthe video game includes (i) cutscene video segments that do not varybased on user-interaction during the rendering and (ii) interactivevideo segments that vary based on user-interaction during the rendering,the operations comprising: obtaining a query fingerprint generated inreal-time during the rendering as a representation of the video gamebeing played, wherein the query fingerprint includes (i) a first portionrepresenting a first cutscene video segment and (ii) a second portionrepresenting a first interactive video segment; detecting a matchbetween the first portion of the query fingerprint and a referencefingerprint that represents the first cutscene video segment and, basedon the detected match, identifying the video game being rendered; afteridentifying the video content being rendered, applying a trained neuralnetwork to at least the second portion of the query fingerprint todetect that the video content being rendered continues to be theidentified game; and responsive to at least detecting that the videocontent being rendered continues to be the identified video game,causing a presentation of supplemental content.
 17. The non-transitorydata storage of claim 16, wherein the operations further comprise:detecting an ending of the detected match, wherein applying the trainedneural network is responsive to at least the detecting of the ending ofthe detected match.
 18. The non-transitory data storage of claim 16,wherein the operations further comprise: determining, based on theapplying of the trained neural network to at least the second portion ofthe query fingerprint, that the first interactive video segment hascontinued for at least a threshold duration, wherein causing thepresentation of supplemental content is further responsive to thedetermining that the interactive portion of the video content hascontinued for at least the threshold duration, and wherein thesupplemental content comprises video-game help content.
 19. A videopresentation device comprising: a video input interface through which toreceive video content to be rendered by the video presentation device; avideo presentation interface for presenting the received video content,wherein the video content includes (i) a pre-established video segmentthat does not vary based on user-interaction during the presenting and(ii) a dynamically-defined video segment that varies based onuser-interaction during the presenting; a network communicationinterface; a processing unit; non-transitory data storage; and programinstructions stored in the non-transitory data storage and executable bythe processing unit to carry out operations comprising: generating aquery fingerprint in real-time during while the video content is beingpresented, the query fingerprint representing the video content beingpresented and including a first portion representing the pre-establishedvideo segment and a second portion representing the dynamically-definedvideo segment, outputting, for transmission through the networkcommunication interface to a computing system, the first portion of thequery fingerprint, wherein the computing system detects a match betweenthe first portion of the query fingerprint and a reference fingerprintthat represents the pre-established video segment and, based on thedetected match, identifies the video content being presented by thevideo presentation device, outputting, for transmission through thenetwork communication interface to the computing system, the secondportion of the query fingerprint, wherein, after identifying the videocontent being rendered, the computing system applies a trained neuralnetwork to at least the second portion of the query fingerprint, anddetects, based on the applying of the neural network, that the videocontent being presented by the video presentation device continues to bethe identified video content, and receiving from the computing system,through the network communication interface, from the computing system adirective for the video presentation device to present supplementalcontent based on the computing system having detected that the videocontent being presented by the video presentation device continues to bethe identified video content.
 20. The video presentation device of claim19, wherein the video content comprises video game content, wherein thepre-established video segment comprises a cutscene video segment,wherein the dynamically-defined video segment comprises an interactivegameplay video segment, and wherein the supplemental content comprisesvideo-game help content.